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NEW QUESTION: 1

A. Option B
B. Option A
C. Option D
D. Option C
Answer: D
Explanation:
AWS Identity and Access Management is a web service which allows organizations to manage users and
user permissions for various AWS services. Whenever the organization is creating an IAM user, there
should be a unique ID for each user. It is not possible to have the same login ID for multiple users. The
names of users, groups, roles, instance profiles must be alphanumeric, including the following common
characters: plus (+), equal (=), comma (,), period (.), at (@), and dash (-).
Reference: http://docs.aws.amazon.com/IAM/latest/UserGuide/Using_SettingUpUser.html

NEW QUESTION: 2
What is the role of a user data store in Oracle Identity Federation (OIF) 11g when it is configured as an Identity Provider (IdP)? (Choose the best answer.)
A. Oracle Identity Federation uses the repository to map the information in received assertions to user identities at the destination.
B. Oracle Identity Federation uses the repository to verify user identities and to build protocol assertions.
C. When creating a new federation, Oracle Identity Federation uses the repository to identify a user and link the new federation ti that user's account.
D. Oracle Identity Federation uses the repository to authorize users for access to protected resources.
Answer: D

NEW QUESTION: 3
Which of the following is not a type of retrieving Collection?
Please choose the correct answer. Response:
A. Expanding
B. Exposing
C. Paging
D. filtering
Answer: B

NEW QUESTION: 4
Powerful Performance Presenters (PPP) is a performance attribution and evaluation firm for pension consulting firms and has recently been hired by Stober and Robertson to conduct a performance attribution analysis for TopTech. Tom Harrison and Wendy Powell are the principals for PPP. Although performance attribution has come under fire lately because of its shortcomings, Stober believes PPP provides a needed service to its clients. Robertson shares Stober's view of performance attribution analysis.
Stober and Robertson request that Harrison and Powell provide a discussion of performance measures.
During a conversation on complements to attribution analysis, Harrison notes the uses of the Treynor ratio.
He states that the Treynor ratio is appropriate only when the investor's portfolio is well diversified. Powell states that the Sharpe ratio and the Treynor ratio will typically yield the same performance rankings for a set of portfolios.
Stober requests that PPP do some performance attribution calculations on TopTech's managers. In order to facilitate the analysis, Stober provides the information in the following table:

Harrison states one of PPP's services is that it will determine if TopTech has chosen a valid benchmark.
Stoher volunteers that indeed his firm's benchmark possesses the properties of a valid benchmark and describes its composition. The benchmark has the following characteristics:
* It uses the top 10% of U.S. portfolio managers each year in each asset class as the benchmark for TopTech managers;
* TopTech is very careful to make sure that its managers are familiar with the securities in each benchmark asset class;
* The identities and weights of various securities in the TopTech benchmark are clearly defined.
During a presentation to Stober, Robertson, and other TopTech executives, Harrison and Powell describe how macro attribution analysis can decompose an entire fund's excess returns into various levels. In his introduction, Robertson delineates the six levels as net contributions, risk-free return, asset categories, benchmarks, investment managers, and allocations effects.
Robertson states that TopTech has performed impressively at the investment managers level tor three years in a row. Harrison and Powell then describe the levels in greater detail. Harrison describes the benchmark level as the difference between active managers' returns and their benchmark returns. Powell states that the investment managers' level reflects the returns to active management on the part of the fund's managers, weighted by the amount actually allocated to each manager.
At the request of Stober, Harrison and Powell explore alternatives to the benchmark TopTech is currently using for its small-cap value manager. After some investigation of the small-cap value manager's emphasis, Harrison and Powell derive four potential custom benchmarks and calculate two measures to evaluate the benchmarks: (1) the return to the manager's active management or A = portfolio return - benchmark return; and (2) the return to the manager's style or S = benchmark return - broad market return.
The following characteristics are presented below for each benchmark: (1) the beta between the benchmark and the small-cap value portfolio; (2) the tracking error (i.e., the standard deviation of A); (3) the turnover of the benchmark; and (4) the correlation between A and S.

Harrison and Powell evaluate the benchmarks based on the four measures.
Based on an overall attribution analysis, does TopTech demonstrate superior ability to select stocks?
A. No, the within-sector selection effect is -4.5%.
B. No, the within-sector selection effect is -3.2%.
C. Yes, the within-sector selection effect is 1.3%.
Answer: A
Explanation:
Explanation/Reference:
Explanation:
The within-sector selection effect measures the manager's ability to select superior securities to represent each sector in the portfolio. It is the sum of the weight for each sector in the benchmark times the difference in that sector's return in the portfolio and in the benchmark.
Within sector selection effect = (0.60)(0.187 - 0.286) + 0.25(0.158 - 0.124) + (0.15) (0.125 - 0.0885) = -
0.0594 + 0.0085 + 0.0055 = -0.0454 = -4.54%.
In sum, in the financial and large cap sectors, the manager chose superior stocks, so they show superior ability there. The overall within-sector selection effect is negative (-4.54%), however, so they do not show a consistent overall ability to select stocks.
The remaining component of attribution analysis (the allocation/selection interaction effect) can be calculated as the difference between the portfolio and benchmark weights for each sector multiplied by the difference between the return for the sector in the portfolio and the return for the sector in the benchmark.
The total allocation/ selection interaction effect is the sum of these products.
Allocation/selection interaction effect = (0.50 - 0.60)(0.187 - 0.286) + (0.30 - 0.25) (0.158 - 0.124) + (0.20 -
0.15)(0.125 - 0.0885) = 0.0099 + 0.0017 + 0.0018 = 0.0134= 1.34%.
The total excess return for the manager is then -1.80% + 1.34% - 4.54% = -5-00%.
This should be equal to the excess return calculated using the total returns for the benchmark and the portfolio. The total return for the benchmark is calculated above as 21.6%. For the portfolio it is: = (0.5) (0.187) + (0.3)(0.158) + (0.2)(0.125) = 16.6%. Thus the excess return calculated using the total returns for the portfolio and the benchmark is 16.6% - 21.6% = -5.0%. (Study Session 17, LOS 47.1)